Enabling Privacy-Preserving Parallel Outsourcing Matrix Inversion in IoT

Abstract

With the rapid development of Internet of Things (IoT), edge computing has been widely applied as a novel computing paradigm. Securely outsourcing intensive tasks to edge servers is becoming increasingly pervasive. It is a nice approach for resource-limited IoT devices to accomplish heavy computing tasks. Matrix inversion is a basic but time-consuming operation, which has a wide range of applications in IoT. The current privacy-preserving outsourcing schemes for matrix inversion cannot support parallel computing based on multiple edge servers. As a result, they cannot well satisfy the requirement of fast response for computation in IoT. In order to deal with this problem, we propose two privacy-preserving parallel outsourcing schemes for matrix inversion in IoT. In the first scheme, we design a novel method to generate a random matrix, which is used to blind the inputted original matrix. In this scheme, two edge servers compute the inversion of the encrypted matrix in parallel to improve the computational efficiency. To further improve the efficiency, we design a novel subtasks partitioning and assignment strategy and propose the second scheme by balancing the computing load of edge servers. We analyze the correctness, security, and verifiability of the proposed schemes. And we provide theoretical analysis and experimental results to demonstrate the performance advantages of the proposed schemes.